Data Engineering & Warehousing Engineer
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Key skills for this role
About the Role
Design, develop, and maintain scalable data platforms and enterprise data warehouses, utilizing ETL/ELT pipelines and cloud technologies for analytics solutions.
Key Skills for This Role
Responsibilities
- Design, develop, and optimize scalable ETL/ELT pipelines for structured and unstructured data
- Build and maintain enterprise data warehouses, data lakes, and modern data platforms
- Develop real time and batch data processing solutions
- Integrate data from multiple internal and external sources while ensuring data quality and governance
- Collaborate with Data Scientists, BI Developers, and business stakeholders to support analytical requirements
- Optimize data storage, query performance, and pipeline reliability
- Implement data security, monitoring, and governance best practices
- Troubleshoot and resolve data pipeline and platform issues
- Participate in architecture discussions and contribute to data platform modernization initiatives
Requirements
- 3 11 years of experience in data engineering and warehousing
- Hands on experience with Google BigQuery, Dataflow, Dataproc, Pub/Sub
- Experience with Azure Synapse and Azure Data Factory
- Experience with Amazon Redshift and AWS Glue
- Strong experience with Apache Spark and Apache Kafka
- Hands on experience with dbt or Oracle Data Integrator (ODI)
- Strong experience with Oracle or PostgreSQL
- Expertise in SQL, relational database design, performance tuning, and query optimization
- Experience with data modeling, data governance, metadata management, and data quality frameworks
- Knowledge of dimensional modeling and modern data warehouse architectures
Full Job Posting
Job Overview
- We are seeking a highly skilled Data Engineering & Warehousing Engineer with 3 11 years of experience to design, develop, and maintain scalable data platforms and enterprise data warehouse solutions.
- The ideal candidate will have hands on expertise in building ETL/ELT pipelines, data integration, cloud based data platforms, and big data processing technologies.
- You will play a key role in enabling reliable, high performance analytics and business intelligence solutions.
Key Responsibilities
- Design, develop, and optimize scalable ETL/ELT pipelines for structured and unstructured data.
- Build and maintain enterprise data warehouses, data lakes, and modern data platforms.
- Develop real time and batch data processing solutions.
- Integrate data from multiple internal and external sources while ensuring data quality and governance.
- Collaborate with Data Scientists, BI Developers, and business stakeholders to support analytical requirements.
- Optimize data storage, query performance, and pipeline reliability.
- Implement data security, monitoring, and governance best practices.
- Troubleshoot and resolve data pipeline and platform issues.
- Participate in architecture discussions and contribute to data platform modernization initiatives.
Required Technical Skills
- Cloud Data Platforms: Hands on experience with Google BigQuery, Dataflow, Dataproc, Pub/Sub; Azure Synapse and Azure Data Factory; Amazon Redshift and AWS Glue.
- Data Processing & Streaming: Strong experience with Apache Spark and Apache Kafka; experience building batch and real time data processing pipelines.
- Data Transformation: Hands on experience with dbt or Oracle Data Integrator (ODI) for data transformation and orchestration; experience implementing ETL/ELT best practices and reusable data models.
- Databases & Data Warehousing: Strong experience with Oracle or PostgreSQL; expertise in SQL, relational database design, performance tuning, and query optimization.
- Data Engineering: Experience with data modeling, data governance, metadata management, and data quality frameworks; knowledge of dimensional modeling and modern data warehouse architectures.
Preferred Skills
- Experience with cloud native data lake and lakehouse architectures.
- Knowledge of CI/CD pipelines and Infrastructure as Code (IaC).
- Familiarity with containerization technologies such as Docker and Kubernetes.
- Experience supporting machine learning and analytics workloads.
- Cloud certifications on AWS, Microsoft Azure, or Google Cloud Platform are a plus.
Key Technology Stack
- Google Cloud Data Services: BigQuery, Dataflow, Dataproc, Pub/Sub
- Azure Data Services: Azure Synapse, Azure Data Factory
- AWS Data Services: Amazon Redshift, AWS Glue
- Data Processing: Apache Spark, Apache Kafka
- Data Transformation: dbt or Oracle Data Integrator (ODI)
- Databases: Oracle or PostgreSQL
- Programming: SQL and Python
- Cloud Platforms: Google Cloud Platform, Microsoft Azure, or Amazon Web Services (Preferred)
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